文章摘要
刘桂锋,郭科远,包翔.基于多源数据融合的新冠肺炎病例活动知识图谱构建与知识发现研究[J].情报工程,2023,9(1):102-117
基于多源数据融合的新冠肺炎病例活动知识图谱构建与知识发现研究
Research on the Construction and Knowledge Discovery of COVID-19 Case Activity Knowledge Map Based on Multi source Data Fusion
  
DOI:10.3772/j.issn.2095-915X.2023.01.008
中文关键词: COVID-19;知识图谱;多源异构数据;数据融合;知识组织;数据管理;数据科学
英文关键词: Covid-19; Knowledge Graph; Multi-source heterogeneous data; data fusion; Knowledge organization; data management; data science
基金项目:国家社会科学基金一般项目“科学数据融合模式设计与体系建构研究”(21BTQ080)。
作者单位
刘桂锋 江苏大学科技信息研究所 镇江 212013 
郭科远 江苏大学科技信息研究所 镇江 212014 
包翔 江苏大学科技信息研究所 镇江 212015 
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中文摘要:
      [ 目的 / 意义 ] 面对复杂多变的疫情状况,为利用好多源异构的互联网数据资源,保证政府精准施策,保障人民生命安全。[ 方法 / 过程 ] 本文利用 Neo4j 图形数据库针对疫情期间病例活动轨迹数据实现了相关的知识图谱的构建与应用,通过 louvain 算法实现对确诊病例的社区划分,分析各个社区内部关系,深入挖掘时空关系,融合政策文本知识图谱用于辅助决策,再融合零散的医疗机构相关信息,生成 XY 市医疗机构知识图谱。[ 结果 / 结论 ] 本文借助知识图谱解决与疫情相关的多源异构数据融合问题,通过构建 COVID-19 病例活动知识图谱分析疫情形势及传播特点,并结合政策文本知识图谱实现了政府辅助决策服务,设计一种快速发现病例的方法。
英文摘要:
      [Purpose/Significance] In the face of the complex and changeable epidemic situation, we should do a good job of tracing the source of flow in order to use many heterogeneous Internet data resources to ensure that the government accurately implements policies and ensures the safety of people’s lives. [Methods/process] This paper uses neo4j graph database to construct and apply relevant knowledge graphs for case activity trajectory data during the epidemic period, realizes the community division of confirmed cases through louvain algorithm, analyzes the internal relationships of various communities, digs deep into the relationship between time and space, integrates the policy text knowledge graph, assists in decision-making, and then integrates scattered medical institution related information to generate a knowledge graph of medical institutions in city of XY. [Results/Conclusions] This paper solves the problem of multi-source heterogeneous data fusion related to the epidemic situation by means of knowledge graph, analyzes the epidemic situation and transmission characteristics by constructing a knowledge graph of COVID-19 case activities, and realizes the government-assisted decision-making service in combination with the policy text knowledge graph, and designs a method to quickly find cases.
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